def _forward(x):
return np.log(x) if x is not None else None
def _inverse(x):
return math.e**x
def spiral(data, unidad, titulo, min=None, max=None, pais=None, region=None, p=0, norm=0, LINEWIDTH=6, EDGEWIDTH=0, CAPSTYLE='projecting', COLORMAP='viridis', ALPHA=1, FIRSTMONTH=0):
estaciones = stations(pais, region)
spiral_data = data.iloc[:,0]
cols = data.columns.to_list()
for st in estaciones:
if st in cols:
spiral_data = pd.concat([spiral_data, data[st]], axis=1)
spiral_data["medicion"] = spiral_data.mean(axis=1)
spiral_data = spiral_data[['fecha','medicion']]
# set origin at the first FIRSTDAY before the first trip, midnight
first_trip = spiral_data["fecha"].min()
origin = first_trip - np.timedelta64(first_trip.month - FIRSTMONTH - 1, 'M')
months = pd.date_range(origin, origin + np.timedelta64(1, 'Y'), freq="M").strftime("%b").tolist()
# convert trip timestamps to week fractions
spiral_data['start'] = (spiral_data['fecha'] - origin) / np.timedelta64(1, 'Y')
spiral_data['stop'] = (spiral_data['fecha'] - origin + np.timedelta64(23, 'h')) / np.timedelta64(1, 'Y')
# sort dataset so shortest trips are plotted last
# should prevent longer events to cover shorter ones, still suboptimal
spiral_data = spiral_data.sort_values("medicion", ascending=False).reset_index()
fig = plt.figure(figsize=(16, 12))
ax = fig.gca(projection="polar")
vmin = spiral_data['medicion'].min() if min is None else min
vmax = spiral_data['medicion'].max() if max is None else max
for idx, event in spiral_data.iterrows():
# sample normalized distance from colormap
if not math.isnan(event['medicion']):
if norm==0:
ndist = (event['medicion']+p - vmin+p) / (vmax+p-vmin+p)
elif norm==1:
ndist = (np.sqrt(event['medicion']+p) - np.sqrt(vmin+p)) / (np.sqrt(vmax+p)-np.sqrt(vmin+p))
else:
ndist = (np.log(event['medicion']+p) - np.log(vmin+p)) / (np.log(vmax+p)-np.log(vmin+p))
color = plt.cm.get_cmap(COLORMAP)(ndist)
else:
color = "White"
tstart, tstop = event.loc[['start', 'stop']]
# timestamps are in week fractions, 2pi is one week
nsamples = int(1000. * (tstop - tstart))
t = np.linspace(tstart, tstop, nsamples)
theta = 2 * np.pi * t
arc, = ax.plot(theta, t, lw=LINEWIDTH, color=color, solid_capstyle=CAPSTYLE, alpha=ALPHA)
if EDGEWIDTH > 0:
arc.set_path_effects([mpe.Stroke(linewidth=LINEWIDTH+EDGEWIDTH, foreground='black'), mpe.Normal()])
# grid and labels
ax.set_rticks([])
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
ax.set_xticks(np.linspace(0, 2*np.pi, 12, endpoint=False))
ax.set_xticklabels(months)
ax.tick_params('x', pad=2)
ax.grid(True)
# setup a custom colorbar, everything's always a bit tricky with mpl colorbars
if norm==0:
norma = mpl.colors.Normalize(vmin=vmin+p, vmax=vmax+p)
elif norm==1:
norma = mpl.colors.PowerNorm(gamma=0.5, vmin=vmin+p, vmax=vmax+p)
else:
norma = mpl.colors.FuncNorm((_forward, _inverse), vmin=vmin+p, vmax=vmax+p)
#norma = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
sm = plt.cm.ScalarMappable(cmap=COLORMAP, norm=norma)
sm.set_array([])
plt.colorbar(sm, ticks=np.linspace(vmin, vmax, 12), fraction=0.04, aspect=60, pad=0.1, label=unidad, ax=ax, extend='max')
plt.title(titulo, size=20, color="Black", y=1.06)
plt.savefig(titulo+".png", pad_inches=0, bbox_inches="tight")
